Box-whisker Plot Research Articles

Normalization of Flow-mediated Dilation to Brachial Artery Material Property: A Feasibility Study.

Endothelial reactivity (ER) is widely measured using flow-mediated dilation (FMD) of brachial artery. Conventional measurement of FMD is influenced by factors such as input shear stress, arterial transmural pressure, diameter and thereby arterial material properties (ε). Thus, for a reliable interpretation of FMD, it has to be normalized with respect to the above confounding factors. Normalization of FMD with shear stress at the time of measurement has been reported to reduce measurement variability. However, its widespread usage among the research community is limited. In this work, we examine the feasibility of normalizing the brachial FMD index (FMD%) to ε : extrema (εp), baseline (εb) and extrema change (∆ε) post-ischemia using its inter-day variability against FMD. In-vivo measurements were performed on 10 participants for 2 consecutive days and simultaneous pressure-diameter cycles were collected to estimate the material properties during reactive hyperemia (RH). The box-whisker plot reveals differences in the mean and deviation of FMD to FMD|εb. A significant value for repeatability (ICC ≥ 0.6) was obtained for normalized FMD (FMD|εb) for specific stiffness index (β), pressure-strain elastic modulus (Ep), and local pulse wave velocity (PWV) as compared to FMD. Hence, normalization of FMD% to arterial ε can potentially improve the measurement reliability of ER assessment.Clinical Relevance- This pilot study demonstrates the feasibility of brachial artery stiffness assessment during FMD and its potential use for normalizing the standard FMD measurement.

A Statistical Synopsis of COVID-19 Components and Descriptive Analysis of Their Socio-Economic and Healthcare Aspects in Bangladesh Perspective.

The aim of the work is to analyze the socio-economic and healthcare aspects that arise in the contemporary COVID-19 situation from Bangladesh perspective. We elaborately discuss the successive COVID-19 occurrences in Bangladesh with consequential information. The components associated with the COVID-19 commencement and treatment policy with corresponding features and their consequences are patently delineated. The effect of troublesome issues related to the treatment is detailed with supporting real-time data. We elucidate the applications of modern technologies advancement in epidemiological aspects and their existent compatibility in Bangladesh. We statistically analyze the real-time data through figurative and tabular approaches. Some relevant measures of central tendency and dispersion are utilized to explore the data structure and its observable specifications. For a clear manifestation, Z- scores of the COVID-19 components are analyzed through the Box-Whisker plot. We have discovered that the gathered data exhibit features that are unsatisfactory for the normal distribution, are highly positively skewed, and are predominated by the earliest occurrences. Infections and deaths were initially lower than the global average, but they drastically rose in the first quarter of 2021 and persisted for the remainder of the year. Substantial preventive results were produced by the region-wisetime-worthy moves. In the fourth quarter of 2021, the infections and deaths noticeably decreased, and the number of recoveries was highly significant. In the middle of 2022, a lethal rise in infections was observed in Bangladesh and that was quickly stabilized, and the pandemic ingredients were under control. According to our assessment, some concluding remarks are made at the end of this work.

Dynamic Extraction of Initial Behavior for Evasive Malware Detection

Recently, malware has become more abundant and complex as the Internet has become more widely used in daily services. Achieving satisfactory accuracy in malware detection is a challenging task since malicious software exhibit non-relevant features when they change the performed behaviors as a result of their awareness of the analysis environments. However, the existing solutions extract features from the entire collected data offered by malware during the run time. Accordingly, the actual malicious behaviors are hidden during the training, leading to a model trained using unrepresentative features. To this end, this study presents a feature extraction scheme based on the proposed dynamic initial evasion behaviors determination (DIEBD) technique to improve the performance of evasive malware detection. To effectively represent evasion behaviors, the collected behaviors are tracked by examining the entropy distributions of APIs-gram features using the box-whisker plot algorithm. A feature set suggested by the DIEBD-based feature extraction scheme is used to train machine learning algorithms to evaluate the proposed scheme. Our experiments’ outcomes on a dataset of benign and evasive malware samples show that the proposed scheme achieved an accuracy of 0.967, false positive rate of 0.040, and F1 of 0.975.

Novel Piecewise Distance Based on Adaptive Region Key-Points Extraction for LCCD With VHR Remote-Sensing Images

Land cover change detection (LCCD) with very high-resolution remote-sensing images (VHR_RSIs) is important in observing surface change on Earth. However, pseudo changes usually reduces the accuracy of the detection map. In this paper, novel piecewise distance based on adaptive region key-points extraction called sparse key-point distance (SKPD) is developed to measure the change magnitude between the bitemporal VHR_RSIs for LCCD. The proposed approach consists of three steps. First, an adaptive region generation algorithm is promoted for exploring spatial-contextual information. Then, the adaptive region around each pixel is sparsely represented with the box-whisker plot theory and the adaptive region is converted into a sparse key point vector. Finally, a piecewise distance is defined to measure the change magnitude between the bi-temporal images. While the entire VHR_RSIs are scanned and the proposed SKPD method proceeds on a pixel by pixel basis, a change magnitude image (CMI) can be generated and a binary threshold method can be applied on the CMI to obtain a change detection map. Experimental results based on four pairs of real VHR_RSIs and four state-of-the-art methods effectively demonstrated the superiority of the proposed approach for achieving LCCD with VHR_RSIs, such as the improvements for the four datasets are 5.25%, 14.76%, 18.13%, and 22.24%, respectively in terms of overall accuracy.

A Retrospective Taiwanese-Population-Based Clinical Study on Determining the Efficacy and Safety of Disposable Circumcision Anastomat.

Traditional approaches for male circumcision are tedious and could lead to complications such as peri-/postoperative pain, bleeding, and infection. Thus, for the first time, we investigated the surgical outcomes of recently the discovered Disposable Circumcision Anastomat Type A (Dongguan ZSR Biomedical Technology Company Ltd., China), a disposable circumcision suture device (DCSD), in terms of the operation time, surgical complexity, safety, satisfaction, and aesthetic outcomes and most importantly the prognostic factors for postoperative infection. A total of 394 individuals were circumcised, with a mean age, body mass index (BMI), stretched penile length (SPL), and penile circumference of 30.1 ± 7.05 years, 25.47 ± 4.73, 10.12 ± 1.61, and 7 ± 0.73 cm, respectively. Associated comorbidities included diabetes mellitus (6.09%), hypertension (2.03%), gout (1.02%), end-stage renal disease (ESRD, 0.25%), and HIV (0.25%). The mean operation time, average postoperative bleeding, and wound infection rate was 31.4 ± 9.96 min, 2.54%, and 9.39%, respectively. The mean VAS postoperative pain scores at D0 and D1 were 4.4 ± 2.4 and 1.9 ± 1.6, respectively. Moreover, 1.27% of subjects required reoperation, and a 2.03% rate of instrument malfunction was noted. The significant factors associated with the post-operative infection group were age (p = 0.0313), BW (p = 0.0081), BMI (p = 0.0026), penile circumference (p = 0.0343), and DM (p ≤ 0.001). Multivariate analyses revealed only DM as a statistically significant factor (p < 0.001). Our box–whisker plot revealed no significant difference between the HbA1c level of infection (Hb1Ac = 7.77 ± 1.39) and non-infection groups (Hb1Ac = 6.92 ± 1.84). However, a trend of higher glycemic index in the infection group was observed. Conclusively, DSCD could be an effective and safe alternative to performing circumcision. However, in the population with advanced aging, phimosis, elevated BMI, and DM (HbA1C > 9%), users should be highly cautious due to the increased risk of infection, dehiscence, and hematoma.

Seismic response prediction of RC bridge piers through stacked long short-term memory network

This paper aims at addressing the prediction of the displacement time histories and subsequently hysteresis curves of reinforced concrete bridge piers using a real-time hybrid simulation. A deep recursive long short-term memory (LSTM) network combined with a bidirectional LSTM, as a powerful deep learning algorithm, is utilized to achieve this goal. The designed stacked LSTM network includes multiple uncertain input variables, horizontal and vertical ground motions, horizontal and vertical actuator loads, the effective height of the bridge pier, the moment of inertia, and the mass assumed for hybrid simulation. The functional application programming interface in the Keras Python library is utilized to develop a complex model with these multiple inputs. Considering all these variables helps to predict the hysteresis curves of reinforced concrete bridge piers more accurately. The database for the training, validation, and unseen dataset of the proposed LSTM network during the prediction process comprises 12 experimental hybrid simulation tests on the bridge piers considering various aforementioned characteristics. The results reveal that the mean square error, as a loss function, is monotonically decreased to zero for both the training and validation datasets after 5000 epochs, and a high level of correlation is observed between the predicted and the reference values of the displacement time series for all the datasets. The Box-Whisker plot and Gaussian distribution of normalized error are also investigated for all the predictions to have more confidence in the estimations. It can be concluded that the maximum mean of the normalized error is about 0.05 for unseen data. Finally, it brings to an end that the LSTM method implemented in this study can successfully reduce the time and experimental costs to conduct new experimental hybrid simulation tests in the future.

Effect of the Volatility of the Crypto Currency and Its Effect on the Market Returns

In this study, cryptocurrency is a type of digital money that uses cryptography to protect transactions, limit the production of new units, and verify asset transfers. The focus of this research is to see how volatile Bitcoin exchange rates and returns. The standard deviation of logarithmic returns is calculated to gauge volatility. The finding of the results was based on the Shapiro-Wilk test that was employed to predict normality in this investigation. In addition, the box-whisker plot and statistical process control chart were used to find high volatility. Volatility is now regarded to be a high value. Eventually, Because of the current high level of volatility, investing in Bitcoin is seen as a high-risk endeavor. The purpose of this study is to assist investors in developing a strategy that maximizes returns while minimizing risk.

Instantaneous wave-free ratio and fractional flow reserve: effect of variation in left ventricular end diastolic pressure

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Among patients with intermediate coronary artery stenosis (50-90%), assessment of functional significance of the lesion by instantaneous wave free ratio (iFR)/ fractional flow reserve (FFR) is recommended in latest guidelines. Though iFR is not much affected by change in hemodynamics compared to FFR, the change in iFR vs FFR due to various hemodynamic factors need a validation. Left ventricular end-diastolic pressure (LVEDP) is one of the hemodynamic factors whose variation and effect on FFR vs iFR is largely unknown. In the present study we evaluated the association of change in LVEDP on the changing pattern of iFR/FFR which may hold a clinical significance especially with percutaneous coronary intervention in heart failure patients. Methods This was a prospective, investigator-initiated, single-center study involving 20 patients with stable coronary artery disease and at least one intermediate coronary lesion (50-90%). The enrolled patients were subjected to both iFR and FFR along with baseline LVEDP measurement. Subsequently, intravenous nitroglycerine infusion was given to reduce LVEDP and corresponding iFR and FFR were re-evaluated. The dynamic changes in iFR and FFR were studied in relation to changes in LVEDP using Pearson’s correlation analysis and linear regression analysis. Results The mean LVEDP was lowered from 16.20 ± 1.54 mmHg to 9.50 ± 1.10 mmHg, the mean iFR and FFR got changed from 0.80 ± 0.12 to 0.76 ± 0.12 mmHg and 0.75 ± 0.09 to 0.72 ± 0.09 mmHg respectively. On Pearson’s correlation analysis, LVEDP change did not show statistically significant correlation (linear relationship) with iFR (p = 0.105, r2 = 0.373) and FFR (p = 0.227, r2 = 0.283) changes across the entire range of stenosis severity and in all vessels. Linear regression analysis did not state any independent correlation between LVEDP and iFR and FFR changes in the study group (p &amp;gt;0.05). The % R2 value for iFR and FFR (as a coefficient of determination) of the regression equation were 13.9% and 8%, which means only these percentages of the total variance in iFR and FFR change were explained by LVEDP changes respectively. There was no serious adverse event related to the procedure. Conclusion To the best of our knowledge, this is the first study comparing the effect of changes in LVEDP on both iFR and FFR simultaneously. In our study, 1 mmHg change in LVEDP was associated with a change in FFR by 0.004 and change in iFR by 0.004 which didn’t reveal any significant association (p = 0.227 and 0.105 respectively). This helps us to put FFR at par with iFR under variable hemodynamics. So either of the variables may be used interchangeably with confidence in varied hemodynamic conditions including patients with heart failure. The correlation was non-significant across entire range of stenosis severity, irrespective of sex, age, diabetes and hypertension. This study sets platform for further research with larger number of heterogeneous patient population. Abstract Figure. Box whisker plot

Reference Interval, Biological Variations and Distribution of Creatinine Level in Fasting Urine: A Cross-sectional Study

Introduction: Serum creatinine, urine creatinine, and rate of urine formation are components of creatinine clearance and are under multiple influences. Of these, urine creatinine has the widest variability, especially in the tropical region. Reference intervals, biological variations, and distributions of urine creatinine in the reference individuals are related and important for clinical interpretations of kidney functions. Aim: To establish the reference intervals, biological variations, and distributions of creatinine in urine formed during morning fasting state from reference individuals in a tropical location. Materials and Methods: This observational cross-sectional study of the sample population was done from January 2017 to November 2021 for the establishment of reference interval. Reference individuals, defined by inclusion and exclusion criteria, were from the population at near sea levels plains, in Thrissur district, Kerala, avoiding the mountainous Western Ghats. Morning fasting, third sample of urine was collected to ensure that the urine collected was formed in the morning (n=156). A second set of 44 urine samples, collected from 10 reference individuals consecutively for 4 to 5 days, was used for estimating the analytical Coefficient of Variation (CVA), within-subject (CVI ), and between-subject (CVG) components of biological variation. Outliers in the creatinine data were identified and removed by the Box-Whisker plot to exclude the subclinical influences that affected the urine creatinine, followed by BoxCox transformation to bring the distribution to Gaussian and reference interval calculations of fasting urine creatinine. Results: As the total sample number was &gt;120, non parametric percentile method was used to calculate the reference interval of 17.14 to 325.89 mg/dL. As the female sample number was between 40 and 120, double-sided, 95% reference interval, based on normal distribution was calculated as 12.41 to 328.71 mg/dL. As the male sample was &lt;50, method of robust calculation was used to define the reference interval of 21.91 to 379.12 mg/dL. The reference interval and biological variation data showed gender differences in the distribution, CVI , and CVG due to lower concentration of fasting urine creatinine in females resulting from lower muscle mass. Conclusion: Reference intervals of creatinine in fasting urine formed in the morning were established. Gender differences were observed in the distribution of data, CVI , and CVG.

Training Samples Enriching Approach for Classification Improvement of VHR Remote Sensing Image

Training samples are usually required to train a classifier for supervised classification of very high spatial resolution (VHR) remote sensing images. However, labeling samples is often a labor-intensive and time-consuming task. To solve this problem, this study integrates histogram distribution analysis, double-window flexible pace search (DFPS), and box–whisker plot (BP) techniques into an iterative algorithm to enrich training samples. The major steps of the proposed algorithm are given as follows. First, to acquire the feature distribution of a class, a histogram of each class (HOC) based on the raw classification map is generated. Second, to cover the spectral heterogeneity of an intraclass, some pixel points in each bin of HOC are selected as the coarse training sample set (CTS). Third, to further purify the CTS, DFPS, and BP techniques are adopted to exclude outlier samples and select the representative samples to signify the corresponding class. Finally, the refined training samples are used to retrain the classifier, and the preceding steps are constructed as an iterative algorithm. Experiments were performed on three real VHR remote sensing images to demonstrate the superiorities of the proposed approach in improving classification performance with respect to the maps obtained directly by the initial training set. In addition, compared with cognate state-of-the-art methods, the proposed approach achieved an approximately 2%–13% improvement in classification accuracy. Code available here: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ImgSciGroup/IEEE-GRSL-GSEA-Code</uri> .

Prediction of meteorological drought by using hybrid support vector regression optimized with HHO versus PSO algorithms.

Drought is considered one of the costliest natural disasters that result in water scarcity and crop damage almost every year. Drought monitoring and forecasting are essential for the efficient management of water resources and sustainability in agriculture. However, the design of a consistent drought prediction model based on the dynamic relationship of the drought index with its antecedent values remains a challenging task. In the present research, the SVR (support vector regression) model was hybridized with two different optimization algorithms namely; Particle Swarm Optimization (PSO) and Harris Hawks Optimization (HHO) for reliable prediction of effective drought index (EDI) 1 month ahead, at different locations of Uttarakhand State of India. The inputs of the models were selected through partial autocorrelation function (PACF) analysis. The output produced by the SVR-HHO and SVR-PSO models was compared with the EDI estimated from observed data using five statistical indicators, i.e., RMSE (Root Mean Square Error), MAE (Mean Absolute Error), COC (Coefficient of Correlation), NSE (Nash-Sutcliffe Efficiency), WI (Willmott Index), and graphical inspection of radar-chart, time-variation plot, box-whisker plot, and Taylor diagram. Appraisal of results indicates that the SVR-HHO model (RMSE = 0.535-0.965, MAE = 0.363-0.622, NSE = 0.558-0.860, COC = 0.760-0.930, and WI = 0.862-0.959) outperformed the SVR-PSO model (RMSE = 0.546-0.967, MAE = 0.372-0.625, NSE = 0.556-0.855, COC = 0.758-0.929, and WI = 0.861-0.956) in predicting EDI. Visual inspection of model performances also showed a better performance of SVR-HHO compared to SVR-PSO in replicating the median, inter-quartile range, spread, and pattern of the EDI estimated from observed rainfall. The results indicate that the hybrid SVR-HHO approach can be utilized for reliable EDI predictions in the study area.

A Correlation-Based Sensing Scheme for Outlier Detection in Cognitive Radio Networks

Cooperative spectrum sensing (CSS) is a vital part of cognitive radio networks, which ensures the existence of the primary user (PU) in the network. However, the presence of malicious users (MUs) highly degrades the performance of the system. In the proposed scheme, each secondary user (SU) reports to the fusion center (FC) with a hard decision of the sensing energy to indicate the existence of the PU. The main contribution of this work deals with MU attacks, specifically spectrum sensing data falsification (SSDF) attacks. In this paper, we propose a correlation-based approach to differentiate between the SUs and the outliers by determining the sensing of each SU, and the average value of sensing information with other SUs, to predict the SSDF attack in the system. The FC determines the abnormality of a SU by determining the similarity for each SU with the remaining SUs by following the proposed scheme and declares the SU as an outlier using the box-whisker plot. The effectiveness of the proposed scheme was demonstrated through simulations.

Multifunctional retention pond for stormwater management: A decision-support model using Analytical Network Process (ANP) and Global Sensitivity Analysis (GSA)

The ever-growing impervious and contaminated surfaces in urban areas result in severe floods, degraded waterways, and stormwater. Water-sensitive strategies and water-sensitive urban design and planning can manage stormwaters through multifunctional retention ponds; however, urban professionals seek an assessment model that evaluates and quantifies the performance of multifunctional retention ponds on stormwater management. This research has developed the Urban Retention Pond Index (URPI) assessment model, a universal multi-layered decision-support tool that constitutes three criteria (C1. Geotechnical functions, C2. Water quality and treatment, and C3. Structural and physical landscaping functions), and twenty sub-criteria. Employing Analytical Network Process (ANP) has determined the weights of indicators, which formulated the URPI index. The ANP result indicated soil investigation (WC1.1 = 0.170), soil retention (WC2.1 = 0.156), and infiltration rate (WC1.2 = 0.108) could extensively impact to the performance of multifunctional retention ponds. To validate the model, it was implemented in the Boneyard Creek retention pond using the Weighted Sum Method. The assessment analysis assigned grade A to this site, meaning, Boneyard Creek pond manages stormwater mainly through Soils Investigation (WC1.1 = 0.150), Soil retention (WC2.1 = 0.144), and Infiltration Rate (WC1.2 = 0.091). Furthermore, Global Sensitivity Analysis (GSA) was conducted to analyze the URPI model’s input–output uncertainty and effect of variations, through a series of methods; Cumulative Distribution Functions (CDF), Probability Density Function (PDF), Scatterplot-Histogram Plot, Box-Whisker Plot, and Parallel Coordination. GSA could support the dominant controls and robust decision-making of the URPI model. GSA results determined that model outputs are empirically distributed with minor regression variance to the theoretical distribution. Most of the outputs fall within the intervals where the mean and median are more significant than the mode. The multiple regression analysis has shown that the three criteria are positively and linearly correlated. The Box-Whisker plots revealed the behaviors of the four mentioned measures are similar. Notably, the Box-Whisker standard error plots indicated the minor errors of the outputs in the whole network of the URPI model. Meanwhile, the Parallel Coordination indicated the largest centrality degrees by the spillway and landscape habitat retention sub-criteria and the largest Eigenvector centralities by soil retention and soil investigation sub-criteria in the whole network.

Geochemical baseline determination and contamination of heavy metals in the urban topsoil of Fuxin City, China

Urban topsoil is the most frequent interface between human society and natural environment. The accumulation of heavy metals in the urban topsoil has a direct effect on residents’ life and health. The geochemical baseline of heavy metals is an objective description of the general level of heavy metals in the urban topsoil. Meanwhile, the determination of geochemical baseline is necessary for regional environmental management, especially in coal cities prone to heavy metal pollution. Heavy metal pollution has become an environmental problem in Fuxin City, China for a long time. To establish the geochemical baseline of heavy metals in the topsoil of Fuxin City and to evaluate the ecological risk of the topsoil, we collected 75 topsoil samples (0–20 cm) and analyzed the concentrations of Cu, Ni, Zn, Pb, Cr, Cd, Hg and As through X-ray fluorescence spectrometry, atomic absorption spectrometry and inductively coupled plasma optical emission spectrometry. We determined the geochemical baseline of heavy metals in the topsoil of Fuxin City by using iteration removal, box-whisker plot, cumulative frequency curve and reference metal normalization; evaluated the contamination risk and ecological risk of the topsoil by using the baseline factor index, Nemerow index and Hakanson potential ecological risk index; and identified the source category of heavy metals in the topsoil by using a pedigree clustering heatmap. Results showed that the geochemical baseline values were 42.86, 89.34, 92.23, 60.55, 145.21, 0.09, 0.08 and 4.17 mg/kg for Cu, Ni, Zn, Pb, Cr, Cd, Hg and As, respectively. The results of Nemerow index and Hakanson potential ecological risk index indicated that the urban topsoil in the study area was slightly contaminated and suffering low potential ecological risk. The main contaminated areas dominated in the middle part and northeast part of the study area, especially in the western Haizhou Strip Mine. The result of baseline factor index indicated that Hg and Cd were the major pollution elements. Using a pedigree clustering heatmap, we divided the sources of these heavy metals into three types: type I for Ni and Cr, largely represented the enrichment of heavy metals from natural sources; type II for Cu, Pb, Zn, Cd and As, mainly represented the enrichment of heavy metals from anthropogenic sources; and type III for Hg, represented the form of both natural and anthropogenic inputs.

The nine ADAMs family members serve as potential biomarkers for immune infiltration in pancreatic adenocarcinoma.

BackgroundThe functional significance of ADAMs family members in the immune infiltration of pancreatic adenocarcinoma (PAAD) awaits elucidation.MethodsADAMs family members with significant expression were identified among differentially expressed genes of PAAD based on The Cancer Genome Atlas (TCGA) database followed by a verification based on the Oncomine database. The correlation of ADAMs in PAAD was estimated with the Spearman’s rho value. The pathway enrichment of ADAMs was performed by STRING and GSEALite, respectively. The protein–protein interaction and Gene Ontology analyses of ADAMs and their similar genes were exanimated in STRING and visualized by Cytoscape. Subsequently, the Box-Whisker plot was used to show a correlation between ADAMs and different tumor grade 1/2/3/4 with Student’s t-test. TIMER was applied to estimate a correlation of ADAMs expressions with immune infiltrates and immune checkpoint blockade (ICB) immunotherapy-related molecules. Furthermore, the effect of copy number variation (CNV) of ADAMs genes was assessed on the immune infiltration levels.ResultADAM8/9/10/12/15/19/28/TS2/TS12 were over-expressed in PAAD. Most of the nine ADAMs had a significant correlation. ADAM8/12/15/19 expression was remarkably increased in the comparison between grade 1 and grade 2/3 of PAAD. ADAM8/9/10/12/19/28/TS2/TS12 had a positive correlation with almost five immune infiltrates. ADAM12/19/TS2/TS12 dramatically related with ICB immunotherapy-related molecules. CNV of ADAMs genes potentially influenced the immune infiltration levels.ConclusionKnowledge of the expression level of the ADAMs family could provide a reasonable strategy for improved immunotherapies to PAAD.

Comparative study of measurement of intraocular pressure in eyes with corneal opacity and normal cornea by Tonopen Avia, rebound tonometer (I-care), and noncontact tonometer

Purpose: The most common indications for keratoplasty are patients with corneal opacity. The presence of glaucoma prior to keratoplasty is an important risk factor, eventually may lead to failure of the corneal graft. Currently, we have insufficient evidence of comparison of intraocular pressure (IOP) measurement in patients with corneal opacity. The purpose of this study is to analyze the IOP measurements obtained with Tonopen (TP) Avia, I-Care rebound tonometer, and noncontact tonometer (NCT) in patients with corneal opacity and normal cornea. Materials and Methods: It is a prospective, comparative study using a convenience cohort of eyes with corneal opacity. Patients having corneal opacity and normal cornea had IOP measured using TP (Avia-Reichert, Depew, Newyork, USA), rebound tonometer (I-care), and NCT (Reichert AT 555). IOP measurements were compared and agreement assessed. Bland–Altman plots, box–whisker plot, and error–bar diagram were used for assessing agreement. Results: Thirty eyes with corneal opacity and thirty eyes with normal cornea of the same patients were included in the study. The mean IOP readings in eyes with normal cornea taken by NCT, rebound tonometer, and TP Avia were 13.71 mm Hg with standard deviation (SD) of 2.73, 14.85 mm Hg with SD of 2.13, and 14.73 mm Hg with SD of 2.27, respectively. The mean IOP readings in eyes with corneal opacity taken by NCT, rebound tonometer, and TP Avia were 17.35 mm Hg with SD of 5.82, 15.83 mm Hg with SD of 3.2, and 15.98 mm Hg with SD of 3.53, respectively. Bland–Altman analysis plots show good agreement between all three devices. Conclusion: IOP measurements in normal cornea showed underestimation by NCT and overestimation by rebound tonometer with TP as standard, whereas in eyes with corneal opacity, IOP measurements showed overestimation by NCT and underestimation by rebound tonometer. TP Avia was proved to be a useful instrument because of its smaller contact area and repeatability.

P1378 Impact of maternal diabetes on fetal left atrial function

Abstract Background/Introduction: Diabetes impacts 12- 14% of pregnancies. Evaluating the impact of maternal diabetes on the fetal heart is challenging due to variable image quality and limited time for structural changes to manifest. Left atrial reservoir strain (ER) is a sensitive marker of early left atrial dysfunction which may elucidate fetal atrial dysfunction resulting from maternal diabetes. Purpose We sought to evaluate if fetal ER can detect impairment of left atrial function in the fetal heart as a result of maternal diabetes. Methods We performed a prospective observational study evaluating patients who were referred to the high risk pregnancy service as a result of maternal diabetes (pre-existing or gestational) and healthy controls. Patients were excluded if adequate imaging of the fetal myocardium was not possible. Baseline characteristics of the mother and cardio-metabolic risk factors were recorded. The fetal echocardiogram focused on the 4 chamber view. We used the 4 chamber view with closure of the mitral valve as the zero reference point (R-R) gating. The strain curves from 6 atrial segments were averaged. Normality of the data was assessed using the Shapiro-Wilk test. The Mann-Whitney U test was used to compare ER between fetuses, whose mothers had diabetes versus those fetuses whose mothers were healthy controls. Results A total of 120 fetal scans were performed. 5 were excluded due to poor image quality, which prevented strain analysis. 115 fetal scans were analysed (87 with maternal diabetes and 27 healthy controls). The diabetic subjects and controls did not defer significantly in age Mean ± SD (31± 5 vs 30± 5, p= 0.81). The mothers with diabetes had a higher body mass index than controls (Median [IQR]) (30.5 kg/m2 [25.1, 35.3] vs 24.6 kg/m2 [22.1, 28.6] p &amp;lt; 0.001). The presence of maternal diabetes resulted in lower fetal ER (28.5% [22.1, 36.2] vs 33.4% [26.6, 41.6] P = 0.01). Figure 1 illustrates the Box-Whisker plot comparing Fetal ER between fetuses exposed to maternal diabetes and fetuses where mothers were healthy controls. Conclusion The presence of maternal diabetes results in impaired fetal left atrial function as measured by fetal left atrial strain (ER). Abstract P1378 Figure 1

Influence of cumulus convection and cloud microphysics parameterizations on the prediction of Western Disturbances

The western disturbances (WD) form over the Mediterranean region as extra-tropical low-pressure systems and lose the frontal structure while moving eastward to reach India. These systems bring cold waves, snowfalls, hailstorms and rain over north and north-west India during post monsoon and winter months. The first part (Part A) of the present paper investigates the performance of Advanced Research WRF (ARW) model with 8 combinations of cloud microphysics and cumulus convection schemes in simulating 20 WD cases. These 20 cases were simulated using a single-domain WRF model of horizontal resolution 27 km. The combination of Lin et al. cloud microphysics scheme and Betts–Miller–Janjic cumulus convection scheme (mp2cu2) performs better than other combinations in simulating temperature at 2 m height and precipitation. The performance of the combination of Ferrier (new Eta) microphysics scheme and Betts-Miller-Janjic cumulus convection scheme (mp3cu2) is very close to that of mp2cu2 combination. Analysis of box-whisker plot also shows that the combinations mp2cu2 and mp3cu2 perform better than others. In the second part (Part B) 10 cases are simulated using a double-nested WRF model with inner and outer domain resolutions 9 km and 27 km, respectively. Four cases of part B are simulated with (mp2cu2 and mp3cu2) and without (mp2cu0 and mp3cu0) cumulus convection schemes to understand the response of cloud microphysics to explicit convection and also to select the best combination of cloud microphysics and cumulus convection scheme. The combination mp2cu2 has lower RMSE of precipitation than other combinations. Remaining six cases were then simulated with the combination of mp2cu2 using the double-nested model. Spatial distribution of model simulated and TRMM estimated precipitation agree well in most of the cases. The domain-averaged RMSE of model-simulated precipitation with respect to TRMM 3B42 V7 estimated precipitation varies from 2.89 to 4.12 cm for the six WD cases. The box-whisker diagram shows that the model overestimates the maximum rainfall amount in most of the cases but it is consistent in simulating precipitation over the model domain for all the six cases.

Application of multivariate technique to evaluate spatial distribution of natural radionuclides along Tamil Nadu coastline, east coast of India

The study focuses on the spatial mapping of naturally occurring radionuclides along entire Tamil Nadu coast, which constitute nearly 30% of the east coast of India. In view of this, a total of 37 beach sediment samples are collected, and spectral measurement was carried out by High Purity Germanium detector. The average activity concentrations were found to be in the range of 34.33 ± 32.94 Bq kg−1 for 238U, 51.55 ± 40.39 Bq kg−1 for 238Th and 282.59 ± 84.45 Bq kg−1 for 40K. The contour maps showed the spatial distributions of each radionuclide and were drawn using the kriging method. Box-whisker plot shows the variation of activity concentrations of radionuclides, which follows the order 40K > 232Th > 238U in all the sediment samples. To understand the complex relationships among the radioactive variables and its environmental classifications, a multivariate statistical technique such as correlation matrices, and cluster analysis were applied to the radioactive data sets.

89. Impiego del software “MIM Maestro” per la segmentazione automatica del distretto pelvi in radioterapia

Purpose The feasibility of adaptive radiotherapy is based on the possibility of rapid contouring and re-planning. This work aims to evaluate if an automatic workflow for pelvis structures segmentation developed with MIM Maestro (MIM Software, Inc., Cleveland, OH) could help to reduce contouring time and inter-operator variability. Methods Four physicians contoured manually the pelvis structures (lymph-nodal CTV, rectum, bladder and femoral heads) of 3 patients (M1) registering the delineation time. The same procedure was repeated after one month (M2). Contours were automatically generated (A) with an atlas-based auto-segmentation workflow developed in MIM Maestro and then corrected by each physician (AC). The editing time was registered. The workflow combined a proprietary 38 subject atlas “High risk prostate” for rectum with two home-made atlases for other structures: one (73 subjects) for bladder and femoral heads, the other (14 subjects) for lymph-nodes. Dice similarity coefficient (DSC) was used to compare contours. Intra-operator variability was assessed by comparison of M1 and M2 contours (test A). Inter-operator variability (test B) was evaluated by comparing the 4 sets of original manual (M1) contours with a “gold standard” (GS). This was generated by applying STAPLE algorithm to the manual (M1) contours. To evaluate any possible reduction of inter-operator variability due to the introduction of auto-contouring, also AC contours were compared (test C). Finally AC editing time was compared to M1 contouring time (test D). Results Results of test A, B and C are summarized in the box-whisker plot. As presumable, inter-operator variability exceeds intra-operator variability and the introduction of auto-contouring reduces inter-operator variability. The results of test D are summarized in the table. An average (4 physicians and 3 patients) time saving of 10% for CTV and 17% for other structures was obtained. Conclusions This work demonstrates the potentiality of auto-contouring to reduce time and inter-operator variability.